OpenRaiser/Intern-Atlas huggingface dataset:
Local Multi-Label Explanations for Random Forest
Venue: arXiv cs.LG 2022
Year: 2022

Multi-label classification is a challenging task, particularly in domains where the number of labels to be predicted is large. Deep neural networks are often effective at multi-label classification of images and textual data. When dealing with tabular data, however, conventional machine learning algorithms, such as tree ensembles, appear to outperform competition. Random forest, being a popular ensemble algorithm, has found use in a wide range of real-world problems. Such problems include fraud detection in the financial domain, crime hotspot detection in the legal sector, and in the biomedical field, disease probability prediction when patient records are accessible. Since they have an impact on people's lives, these domains usually require decision-making systems to be explainable. Random Forest falls short on this property, especially when a large number of tree predictors are used. This issue was addressed in a recent research named LionForests, regarding single label classification and regression. In this work, we adapt this technique to multi-label classification problems, by employing three different strategies regarding the labels that the explanation covers. Finally, we provide a set of qualitative and quantitative experiments to assess the efficacy of this approach.
An almost linear time complexity algorithm for the Tool Loading Problem
Venue: arXiv cs.DS 2022
Year: 2022

As shown by Tang, Denardo [9] the job Sequencing and tool Switching Problem (SSP) can be decomposed into the following two problems. Firstly, the Tool Loading Problem (TLP) - for a given sequence of jobs, find an optimal sequence of magazine states that minimizes the total number of tool switches. Secondly, the Job Sequencing Problem (JeSP) - find a sequence of jobs minimizing the total number of tool switches. Published in 1988, the well known Keep Tool Needed Soonest (KTNS) algorithm for solving the TLP has time complexity $O(mn)$. Here $m$ is the total number of tools necessary to complete all $n$ sequenced jobs on a single machine. A tool switch is needed since the tools required to complete all jobs cannot fit in the magazine, whose capacity $C < m$. We hereby propose a new Greedy Pipe Construction Algorithm (GPCA) with time complexity $O(Cn)$. Our new algorithm outperforms KTNS algorithm on large-scale datasets by at least an order of magnitude in terms of CPU times.
PRoA: A Probabilistic Robustness Assessment against Functional Perturbations
Venue: arXiv cs.LG 2022
Year: 2022

In safety-critical deep learning applications robustness measurement is a vital pre-deployment phase. However, existing robustness verification methods are not sufficiently practical for deploying machine learning systems in the real world. On the one hand, these methods attempt to claim that no perturbations can ``fool'' deep neural networks (DNNs), which may be too stringent in practice. On the other hand, existing works rigorously consider $L_p$ bounded additive perturbations on the pixel space, although perturbations, such as colour shifting and geometric transformations, are more practically and frequently occurring in the real world. Thus, from the practical standpoint, we present a novel and general {\it probabilistic robustness assessment method} (PRoA) based on the adaptive concentration, and it can measure the robustness of deep learning models against functional perturbations. PRoA can provide statistical guarantees on the probabilistic robustness of a model, \textit{i.e.}, the probability of failure encountered by the trained model after deployment. Our experiments demonstrate the effectiveness and flexibility of PRoA in terms of evaluating the probabilistic robustness against a broad range of functional perturbations, and PRoA can scale well to various large-scale deep neural networks compared to existing state-of-the-art baselines. For the purpose of reproducibility, we release our tool on GitHub: \url{ https://github.com/TrustAI/PRoA}.
Towards a Tectonic Traffic Shift? Investigating Apple's New Relay Network
Venue: arXiv cs.NI 2022
Year: 2022

Apple recently published its first Beta of the iCloud Private Relay, a privacy protection service with promises resembling the ones of VPNs. The architecture consists of two layers (ingress and egress), operated by disjoint providers. The service is directly integrated into Apple's operating systems and therefore provides a low entry level barrier for a large user base. It seems to be set up for major adoption with its relatively moderate entry-level price. This paper analyzes the iCloud Private Relay from a network perspective and its effect on the Internet and future measurement-based research. We perform EDNS0 Client Subnet DNS queries to collect ingress relay addresses and find 1586 IPv4 addresses. Supplementary RIPE Atlas DNS measurements reveal 1575 IPv6 addresses. Knowledge about these addresses helps to passively detect clients communicating through the relay network. According to our scans, from January through April, ingress addresses grew by 20%. The analysis of our scans through the relay network verifies Apple's claim of rotating egress addresses. Nevertheless, it reveals that ingress and egress relays can be located in the same autonomous system, thus sharing similar routes, potentially allowing traffic correlation.
Deterministic Decoupling of Global Features and its Application to Data Analysis
Venue: arXiv cs.LG 2022
Year: 2022

We introduce a method for deterministic decoupling of global features and show its applicability to improve data analysis performance, as well as to open new venues for feature transfer. We propose a new formalism that is based on defining transformations on submanifolds, by following trajectories along the features gradients. Through these transformations we define a normalization that, we demonstrate, allows for decoupling differentiable features. By applying this to sampling moments, we obtain a quasi-analytic solution for the orthokurtosis, a normalized version of the kurtosis that is not just decoupled from mean and variance, but also from skewness. We apply this method in the original data domain and at the output of a filter bank to regression and classification problems based on global descriptors, obtaining a consistent and significant improvement in performance as compared to using classical (non-decoupled) descriptors.
Probability density estimation for sets of large graphs with respect to spectral information using stochastic block models
Venue: arXiv cs.LG 2022
Year: 2022

For graph-valued data sampled iid from a distribution $μ$, the sample moments are computed with respect to a choice of metric. In this work, we equip the set of graphs with the pseudo-metric defined by the $\ell_2$ norm between the eigenvalues of the respective adjacency matrices. We use this pseudo metric and the respective sample moments of a graph valued data set to infer the parameters of a distribution $\hatμ$ and interpret this distribution as an approximation of $μ$. We verify experimentally that complex distributions $μ$ can be approximated well taking this approach.
Activation Template Matching Loss for Explainable Face Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets? In this paper, we propose a generic Explainable Channel Loss (ECLoss) to construct an explainable face recognition network. The explainable network trained with ECLoss can easily learn the facial part-based representation on the target convolutional layer, where an individual channel can detect a certain face part. Our experiments on dozens of datasets show that ECLoss achieves superior explainability metrics, and at the same time improves the performance of face verification without face alignment. In addition, our visualization results also illustrate the effectiveness of the proposed ECLoss.
ST-CoNAL: Consistency-Based Acquisition Criterion Using Temporal Self-Ensemble for Active Learning
Venue: arXiv cs.CV 2022
Year: 2022

Modern deep learning has achieved great success in various fields. However, it requires the labeling of huge amounts of data, which is expensive and labor-intensive. Active learning (AL), which identifies the most informative samples to be labeled, is becoming increasingly important to maximize the efficiency of the training process. The existing AL methods mostly use only a single final fixed model for acquiring the samples to be labeled. This strategy may not be good enough in that the structural uncertainty of a model for given training data is not considered to acquire the samples. In this study, we propose a novel acquisition criterion based on temporal self-ensemble generated by conventional stochastic gradient descent (SGD) optimization. These self-ensemble models are obtained by capturing the intermediate network weights obtained through SGD iterations. Our acquisition function relies on a consistency measure between the student and teacher models. The student models are given a fixed number of temporal self-ensemble models, and the teacher model is constructed by averaging the weights of the student models. Using the proposed acquisition criterion, we present an AL algorithm, namely student-teacher consistency-based AL (ST-CoNAL). Experiments conducted for image classification tasks on CIFAR-10, CIFAR-100, Caltech-256, and Tiny ImageNet datasets demonstrate that the proposed ST-CoNAL achieves significantly better performance than the existing acquisition methods. Furthermore, extensive experiments show the robustness and effectiveness of our methods.
Array Camera Image Fusion using Physics-Aware Transformers
Venue: arXiv cs.CV 2022
Year: 2022

We demonstrate a physics-aware transformer for feature-based data fusion from cameras with diverse resolution, color spaces, focal planes, focal lengths, and exposure. We also demonstrate a scalable solution for synthetic training data generation for the transformer using open-source computer graphics software. We demonstrate image synthesis on arrays with diverse spectral responses, instantaneous field of view and frame rate.
Pretraining on Interactions for Learning Grounded Affordance Representations
Venue: arXiv cs.CL 2022
Year: 2022

Lexical semantics and cognitive science point to affordances (i.e. the actions that objects support) as critical for understanding and representing nouns and verbs. However, study of these semantic features has not yet been integrated with the "foundation" models that currently dominate language representation research. We hypothesize that predictive modeling of object state over time will result in representations that encode object affordance information "for free". We train a neural network to predict objects' trajectories in a simulated interaction and show that our network's latent representations differentiate between both observed and unobserved affordances. We find that models trained using 3D simulations from our SPATIAL dataset outperform conventional 2D computer vision models trained on a similar task, and, on initial inspection, that differences between concepts correspond to expected features (e.g., roll entails rotation). Our results suggest a way in which modern deep learning approaches to grounded language learning can be integrated with traditional formal semantic notions of lexical representations.
Can We Effectively Use Smart Contracts to Stipulate Time Constraints?
Venue: arXiv cs.DC 2022
Year: 2022

Smart contracts provide the means to stipulate rules of interaction between mutually distrustful organizations. They encode contractual agreements on the basis of source code, which else need to be contractualized in natural language. While the mediation of contractual agreements via smart contracts is seamless in theory, it requires that the conditions of an interaction are accurately made available in the blockchain. Time is a prominent such condition. In the paper at hand, we empirically measure the consistency of a smart contract to yield equal results on the basis of the time of an interaction and its potentially inaccurate representation in the blockchain. We propose a novel metric called execution accuracy to measure this consistency. We specifically measure the execution accuracy of a time interval-constrained smart contract that executes distinct logic within and without some constraint interval. We run experiments for the local Ganache and Quorum and the public Görli and Rinkeby Ethereum blockchains. Our experiments confirm our intuition that execution accuracy decreases near interval bounds. The novelty of our proposed metric resides in its capacity to quantify this decrease and make distinct blockchains comparable with respect to their capacity to accurately stipulate time contraints.
Multi-Label Retinal Disease Classification using Transformers
Venue: arXiv cs.CV 2022
Year: 2022

Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification. Next, a sequence of post-processing steps is applied to ensure the quality of the image data and the range of diseases, present in the dataset. For the first time in fundus multi-label disease classification, a transformer-based model optimized through extensive experimentation is used for image analysis and decision making. Numerous experiments are performed to optimize the configuration of the proposed system. It is shown that the approach performs better than state-of-the-art works on the same task by 7.9% and 8.1% in terms of AUC score for disease detection and disease classification, respectively. The obtained results further support the potential applications of transformer-based architectures in the medical imaging field.
Mechanical Search on Shelves with Efficient Stacking and Destacking of Objects
Venue: arXiv cs.RO 2022
Year: 2022

Stacking increases storage efficiency in shelves, but the lack of visibility and accessibility makes the mechanical search problem of revealing and extracting target objects difficult for robots. In this paper, we extend the lateral-access mechanical search problem to shelves with stacked items and introduce two novel policies -- Distribution Area Reduction for Stacked Scenes (DARSS) and Monte Carlo Tree Search for Stacked Scenes (MCTSSS) -- that use destacking and restacking actions. MCTSSS improves on prior lookahead policies by considering future states after each potential action. Experiments in 1200 simulated and 18 physical trials with a Fetch robot equipped with a blade and suction cup suggest that destacking and restacking actions can reveal the target object with 82--100% success in simulation and 66--100% in physical experiments, and are critical for searching densely packed shelves. In the simulation experiments, both policies outperform a baseline and achieve similar success rates but take more steps compared with an oracle policy that has full state information. In simulation and physical experiments, DARSS outperforms MCTSSS in median number of steps to reveal the target, but MCTSSS has a higher success rate in physical experiments, suggesting robustness to perception noise. See https://sites.google.com/berkeley.edu/stax-ray for supplementary material.
Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics
Venue: arXiv cs.CV 2022
Year: 2022

Measuring the similarity of images is a fundamental problem to computer vision for which no universal solution exists. While simple metrics such as the pixel-wise L2-norm have been shown to have significant flaws, they remain popular. One group of recent state-of-the-art metrics that mitigates some of those flaws are Deep Perceptual Similarity (DPS) metrics, where the similarity is evaluated as the distance in the deep features of neural networks. However, DPS metrics themselves have been less thoroughly examined for their benefits and, especially, their flaws. This work investigates the most common DPS metric, where deep features are compared by spatial position, along with metrics comparing the averaged and sorted deep features. The metrics are analyzed in-depth to understand the strengths and weaknesses of the metrics by using images designed specifically to challenge them. This work contributes with new insights into the flaws of DPS, and further suggests improvements to the metrics. An implementation of this work is available online: https://github.com/guspih/deep_perceptual_similarity_analysis/
Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps
Venue: arXiv cs.CV 2022
Year: 2022

Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications, such as the detection of unsafe situations or the study of mutual interactions for statistical and social purposes. In this paper, we propose a non-invasive and light-invariant framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera. The method can be applied to any robot without requiring hardware access to the internal states. We introduce a novel representation of the predicted pose, namely Semi-Perspective Decoupled Heatmaps (SPDH), to accurately compute 3D joint locations in world coordinates adapting efficient deep networks designed for the 2D Human Pose Estimation. The proposed approach, which takes as input a depth representation based on XYZ coordinates, can be trained on synthetic depth data and applied to real-world settings without the need for domain adaptation techniques. To this end, we present the SimBa dataset, based on both synthetic and real depth images, and use it for the experimental evaluation. Results show that the proposed approach, made of a specific depth map representation and the SPDH, overcomes the current state of the art.
The Role of Complex NLP in Transformers for Text Ranking?
Venue: arXiv cs.CL 2022
Year: 2022

Even though term-based methods such as BM25 provide strong baselines in ranking, under certain conditions they are dominated by large pre-trained masked language models (MLMs) such as BERT. To date, the source of their effectiveness remains unclear. Is it their ability to truly understand the meaning through modeling syntactic aspects? We answer this by manipulating the input order and position information in a way that destroys the natural sequence order of query and passage and shows that the model still achieves comparable performance. Overall, our results highlight that syntactic aspects do not play a critical role in the effectiveness of re-ranking with BERT. We point to other mechanisms such as query-passage cross-attention and richer embeddings that capture word meanings based on aggregated context regardless of the word order for being the main attributions for its superior performance.
Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Shape information is a strong and valuable prior in segmenting organs in medical images. However, most current deep learning based segmentation algorithms have not taken shape information into consideration, which can lead to bias towards texture. We aim at modeling shape explicitly and using it to help medical image segmentation. Previous methods proposed Variational Autoencoder (VAE) based models to learn the distribution of shape for a particular organ and used it to automatically evaluate the quality of a segmentation prediction by fitting it into the learned shape distribution. Based on which we aim at incorporating VAE into current segmentation pipelines. Specifically, we propose a new unsupervised domain adaptation pipeline based on a pseudo loss and a VAE reconstruction loss under a teacher-student learning paradigm. Both losses are optimized simultaneously and, in return, boost the segmentation task performance. Extensive experiments on three public Pancreas segmentation datasets as well as two in-house Pancreas segmentation datasets show consistent improvements with at least 2.8 points gain in the Dice score, demonstrating the effectiveness of our method in challenging unsupervised domain adaptation scenarios for medical image segmentation. We hope this work will advance shape analysis and geometric learning in medical imaging.
5/6G: Networks of the Future or Defuturing Networks?
Venue: arXiv cs.NI 2022
Year: 2022

Energy efficiency is at the core of sustainability solutions for 5/6G networks. We argue this is a too narrow perspective on sustainability, as it ignores the effects of the increased traffic demand these networks stimulate and the need for additional equipment that this demand requires. The hope is that techniques to reduce the network's energy consumption in operation will be able to compensate for increases in traffic demand. However, there are more challenges than just reducing the energy that the network requires to function and it is not clear whether higher energy efficiency will be able to cope with increasing demand. The need for more equipment related to deployment of 5/6G networks may result in important environmental impacts: i) increased pressures on material extraction, which imply socio-environmental conflicts, ecosystem destruction and displacement, ii) more manufacturing and shipment, with effects on greenhouse gas emissions and pollution; iii) increased disposal complexities and challenges to recycle components of such equipments. By extending our view on sustainability to include the aforementioned often ignored implications, we are able to identify design requirements and technical pillars of 5/6G networks that need to be rethought. We also devise new paths forward to address these challenges. We argue that it is crucial to think of alternative applications and requirements that aim to serve user demands explicitly, instead of incessantly creating new needs. We also claim that acknowledging material limitations in the production of new hardware is critical, promoting retrofitting and modular design in future network development. The conclusions of this article show that it is time to start rethinking the course of mobile network development in order to align it to current environmental objectives to tackle the climate emergency the world is experiencing.
Computing NP-hard Repetitiveness Measures via MAX-SAT
Venue: arXiv cs.DS 2022
Year: 2022

Repetitiveness measures reveal profound characteristics of datasets, and give rise to compressed data structures and algorithms working in compressed space. Alas, the computation of some of these measures is NP-hard, and straight-forward computation is infeasible for datasets of even small sizes. Three such measures are the smallest size of a string attractor, the smallest size of a bidirectional macro scheme, and the smallest size of a straight-line program. While a vast variety of implementations for heuristically computing approximations exist, exact computation of these measures has received little to no attention. In this paper, we present MAX-SAT formulations that provide the first non-trivial implementations for exact computation of smallest string attractors, smallest bidirectional macro schemes, and smallest straight-line programs. Computational experiments show that our implementations work for texts of length up to a few hundred for straight-line programs and bidirectional macro schemes, and texts even over a million for string attractors.